{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "LLR_NN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.9"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GCC8LUEjEAQe"
      },
      "source": [
        "# Begin the Code and Mount Google Drive to access files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ct22UcTpDwzy",
        "outputId": "ca5f5901-2f0c-44f9-9686-b12b7b0c634e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "import os\n",
        "\n",
        "# Comment these lines out if not using google colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')\n",
        "os.chdir('/content/gdrive/My Drive/LLR_NN')\n",
        "\n",
        "!ls"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive/; to attempt to forcibly remount, call drive.mount(\"/content/gdrive/\", force_remount=True).\n",
            "bpsk_model.json\t\t data_files_single_snr_fade  llr_bpsk_model_single.h5\n",
            "bpsk_single.json\t fdeep_model.json\t     llr_model.h5\n",
            "data_files_multiple_snr  keras_export\t\t     result_figures\n",
            "data_files_single_snr\t llr_bpsk_model.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Go5CYpgKofbM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Can comment the pip install if not using GPU\n",
        "# Note that commenting this line out if your computer has a GPU will result in slower training times\n",
        "\n",
        "# comment the %%capture line if not on google colab, this is to supress the output\n",
        "%%capture\n",
        "!pip install tensorflow-gpu==2.1.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7LEMKNwNEu6-"
      },
      "source": [
        "## Import Required Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ouqr1PXfEpGs",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import scipy.io as sio\n",
        "import sklearn\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout, Embedding, LSTM, MaxPooling1D\n",
        "from tensorflow.keras.layers import Activation,Conv2D, Flatten, Permute, Cropping2D\n",
        "from tensorflow.keras.layers import ZeroPadding2D, MaxPooling2D, AveragePooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization, Reshape\n",
        "from tensorflow.keras.layers import Conv1D, GRU\n",
        "from tensorflow.keras.layers import ConvLSTM2D\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import initializers\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ytCIzXfJBnKk"
      },
      "source": [
        "# Load the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Vd8TfxeeBlP_",
        "outputId": "c1721262-9864-4fb2-a52c-0312f0e5c82b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "MODULATION = \"16_QAM\"\n",
        "SNR = \"1.8\"\n",
        "FADE_VAR = \"0.01\"\n",
        "SNR_RANGE_MIN = \"0\"\n",
        "SNR_RANGE_MAX = \"10\"\n",
        "\n",
        "# Load the training data\n",
        "X_train_single_SNR = np.genfromtxt(\"./data_files_single_snr/\" + MODULATION + \"_X_train_snr_\" + SNR + \".csv\", delimiter=',')\n",
        "y_train_single_SNR = np.genfromtxt(\"./data_files_single_snr/\" + MODULATION + \"_y_train_snr_\" + SNR + \".csv\", delimiter=',')\n",
        "X_train_multiple_SNR = np.genfromtxt(\"./data_files_multiple_snr/\" + MODULATION + \"_X_train_snr_range_\" + SNR_RANGE_MIN + \"_\" + SNR_RANGE_MAX + \".csv\", delimiter=',')\n",
        "y_train_multiple_SNR = np.genfromtxt(\"./data_files_multiple_snr/\" + MODULATION + \"_y_train_snr_range_\" + SNR_RANGE_MIN + \"_\" + SNR_RANGE_MAX + \".csv\", delimiter=',')\n",
        "X_train_single_SNR_fade = np.genfromtxt(\"./data_files_single_snr_fade/\" + MODULATION + \"_X_train_snr_\" + SNR + \"_fade_var_\" + FADE_VAR + \".csv\", delimiter=',')\n",
        "y_train_single_SNR_fade = np.genfromtxt(\"./data_files_single_snr_fade/\" + MODULATION + \"_y_train_snr_\" + SNR + \"_fade_var_\" + FADE_VAR + \".csv\", delimiter=',')\n",
        "\n",
        "# Load validation data\n",
        "X_valid_single_SNR = np.genfromtxt(\"./data_files_single_snr/\" + MODULATION + \"_X_valid_snr_\" + SNR + \".csv\", delimiter=',')\n",
        "y_valid_single_SNR = np.genfromtxt(\"./data_files_single_snr/\" + MODULATION + \"_y_valid_snr_\" + SNR + \".csv\", delimiter=',')\n",
        "X_valid_multiple_SNR = np.genfromtxt(\"./data_files_multiple_snr/\" + MODULATION + \"_X_valid_snr_range_\" + SNR_RANGE_MIN + \"_\" + SNR_RANGE_MAX + \".csv\", delimiter=',')\n",
        "y_valid_multiple_SNR = np.genfromtxt(\"./data_files_multiple_snr/\" + MODULATION + \"_y_valid_snr_range_\" + SNR_RANGE_MIN + \"_\" + SNR_RANGE_MAX + \".csv\", delimiter=',')\n",
        "X_valid_single_SNR_fade = np.genfromtxt(\"./data_files_single_snr_fade/\" + MODULATION + \"_X_valid_snr_\" + SNR + \"_fade_var_\" + FADE_VAR + \".csv\", delimiter=',')\n",
        "y_valid_single_SNR_fade = np.genfromtxt(\"./data_files_single_snr_fade/\" + MODULATION + \"_y_valid_snr_\" + SNR + \"_fade_var_\" + FADE_VAR + \".csv\", delimiter=',')\n",
        "\n",
        "# Load the test data\n",
        "X_test_single_SNR = np.genfromtxt(\"./data_files_single_snr/\" + MODULATION + \"_X_test_snr_\" + SNR + \".csv\", delimiter=',')\n",
        "y_test_single_SNR = np.genfromtxt(\"./data_files_single_snr/\" + MODULATION + \"_y_test_snr_\" + SNR + \".csv\", delimiter=',')\n",
        "X_test_multiple_SNR = np.genfromtxt(\"./data_files_multiple_snr/\" + MODULATION + \"_X_test_snr_range_\" + SNR_RANGE_MIN + \"_\" + SNR_RANGE_MAX + \".csv\", delimiter=',')\n",
        "y_test_multiple_SNR = np.genfromtxt(\"./data_files_multiple_snr/\" + MODULATION + \"_y_test_snr_range_\" + SNR_RANGE_MIN + \"_\" + SNR_RANGE_MAX + \".csv\", delimiter=',')\n",
        "X_test_single_SNR_fade = np.genfromtxt(\"./data_files_single_snr_fade/\" + MODULATION + \"_X_test_snr_\" + SNR + \"_fade_var_\" + FADE_VAR + \".csv\", delimiter=',')\n",
        "y_test_single_SNR_fade = np.genfromtxt(\"./data_files_single_snr_fade/\" + MODULATION + \"_y_test_snr_\" + SNR + \"_fade_var_\" + FADE_VAR + \".csv\", delimiter=',')\n",
        "\n",
        "# Reshape the data\n",
        "X_train_single_SNR = X_train_single_SNR.T\n",
        "X_valid_single_SNR = X_valid_single_SNR.T\n",
        "X_test_single_SNR = X_test_single_SNR.T\n",
        "X_train_multiple_SNR = X_train_multiple_SNR.T\n",
        "X_valid_multiple_SNR = X_valid_multiple_SNR.T\n",
        "X_test_multiple_SNR = X_test_multiple_SNR.T\n",
        "X_train_single_SNR_fade = X_train_single_SNR_fade.T\n",
        "X_valid_single_SNR_fade = X_valid_single_SNR_fade.T\n",
        "X_test_single_SNR_fade = X_test_single_SNR_fade.T\n",
        "\n",
        "y_train_single_SNR = y_train_single_SNR.T\n",
        "y_valid_single_SNR = y_valid_single_SNR.T\n",
        "y_test_single_SNR = y_test_single_SNR.T\n",
        "y_train_multiple_SNR = y_train_multiple_SNR.T\n",
        "y_valid_multiple_SNR = y_valid_multiple_SNR.T\n",
        "y_test_multiple_SNR = y_test_multiple_SNR.T\n",
        "y_train_single_SNR_fade = y_train_single_SNR_fade.T\n",
        "y_valid_single_SNR_fade = y_valid_single_SNR_fade.T\n",
        "y_test_single_SNR_fade = y_test_single_SNR_fade.T\n",
        "\n",
        "# Special case for M=2 since the data imported was only 1D\n",
        "if(len(y_train_single_SNR.shape) == 1):\n",
        "  y_train_single_SNR = y_train_single_SNR.reshape([-1,1])\n",
        "  y_valid_single_SNR = y_valid_single_SNR.reshape([-1,1])\n",
        "  y_test_single_SNR = y_test_single_SNR.reshape([-1,1])\n",
        "  y_train_multiple_SNR = y_train_multiple_SNR.reshape([-1,1])\n",
        "  y_valid_multiple_SNR = y_valid_multiple_SNR.reshape([-1,1])\n",
        "  y_test_multiple_SNR = y_test_multiple_SNR.reshape([-1,1])\n",
        "  y_train_single_SNR_fade = y_train_single_SNR_fade.reshape([-1,1])\n",
        "  y_valid_single_SNR_fade = y_valid_single_SNR_fade.reshape([-1,1])\n",
        "  y_test_single_SNR_fade = y_test_single_SNR_fade.reshape([-1,1])\n",
        "\n",
        "print(\"Training data single SNR shape: \\t\\t\", X_train_single_SNR.shape)\n",
        "print(\"Validation data single SNR shape: \\t\\t\",X_valid_single_SNR.shape)\n",
        "print(\"Test data single SNR shape: \\t\\t\\t\" ,X_test_single_SNR.shape)\n",
        "\n",
        "print()\n",
        "print(\"Training label single SNR shape: \\t\\t\", y_train_single_SNR.shape)\n",
        "print(\"Validation label single SNR shape: \\t\\t\", y_valid_single_SNR.shape)\n",
        "print(\"Test label single SNR shape: \\t\\t\\t\", y_test_single_SNR.shape)\n",
        "\n",
        "print(\"--------------------------------------------------------------------------\")\n",
        "print()\n",
        "print(\"Training data multiple SNR shape: \\t\\t\", X_train_multiple_SNR.shape)\n",
        "print(\"Validation data multiple SNR shape: \\t\\t\",X_valid_multiple_SNR.shape)\n",
        "print(\"Test data multiple SNR shape: \\t\\t\\t\" ,X_test_multiple_SNR.shape)\n",
        "\n",
        "print()\n",
        "print(\"Training label multiple SNR shape: \\t\\t\", y_train_multiple_SNR.shape)\n",
        "print(\"Validation label multiple SNR shape: \\t\\t\", y_valid_multiple_SNR.shape)\n",
        "print(\"Test label multiple SNR shape: \\t\\t\\t\", y_test_multiple_SNR.shape)\n",
        "\n",
        "print(\"--------------------------------------------------------------------------\")\n",
        "print()\n",
        "print(\"Training data with fading shape: \\t\\t\", X_train_single_SNR_fade.shape)\n",
        "print(\"Validation data with fading shape: \\t\\t\",X_valid_single_SNR_fade.shape)\n",
        "print(\"Test data with fading shape: \\t\\t\\t\" ,X_test_single_SNR_fade.shape)\n",
        "\n",
        "print()\n",
        "print(\"Training label with fading shape: \\t\\t\", y_train_single_SNR_fade.shape)\n",
        "print(\"Validation label with fading shape: \\t\\t\", y_valid_single_SNR_fade.shape)\n",
        "print(\"Test label with fading shape: \\t\\t\\t\", y_test_single_SNR_fade.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data single SNR shape: \t\t (10000, 2)\n",
            "Validation data single SNR shape: \t\t (1500, 2)\n",
            "Test data single SNR shape: \t\t\t (3000, 2)\n",
            "\n",
            "Training label single SNR shape: \t\t (10000, 4)\n",
            "Validation label single SNR shape: \t\t (1500, 4)\n",
            "Test label single SNR shape: \t\t\t (3000, 4)\n",
            "--------------------------------------------------------------------------\n",
            "\n",
            "Training data multiple SNR shape: \t\t (5500, 2)\n",
            "Validation data multiple SNR shape: \t\t (1650, 2)\n",
            "Test data multiple SNR shape: \t\t\t (3300, 2)\n",
            "\n",
            "Training label multiple SNR shape: \t\t (5500, 4)\n",
            "Validation label multiple SNR shape: \t\t (1650, 4)\n",
            "Test label multiple SNR shape: \t\t\t (3300, 4)\n",
            "--------------------------------------------------------------------------\n",
            "\n",
            "Training data with fading shape: \t\t (10000, 2)\n",
            "Validation data with fading shape: \t\t (1500, 2)\n",
            "Test data with fading shape: \t\t\t (3000, 2)\n",
            "\n",
            "Training label with fading shape: \t\t (10000, 4)\n",
            "Validation label with fading shape: \t\t (1500, 4)\n",
            "Test label with fading shape: \t\t\t (3000, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "v-WAWm7SnsA2"
      },
      "source": [
        "# Construct Fully Connected Neural Net trained on a single SNR\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6zkFEwQ2BCcY"
      },
      "source": [
        "## Define the first model. Note that we have multiple outputs corresponding to each bit LLR."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-xcVaRlk15Oe",
        "outputId": "7286bb9a-9bdd-431e-8284-131c21eba234",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "source": [
        "model_input = Input(shape=(X_train_single_SNR.shape[1]), name='model_input')\n",
        "num_outputs = y_train_single_SNR.shape[1]\n",
        "num_layers = 7\n",
        "\n",
        "# Create Dense Layers; these layers are shared between ALL outputs\n",
        "x = Dense(256, activation='relu')(model_input)\n",
        "for i in range(num_layers, 1, -1):\n",
        "  x = Dense(max(num_outputs*2, 2**i), activation=\"relu\")(x)\n",
        "\n",
        "# Create more Dense layers that are unique to each output\n",
        "output_vec = [None]*num_outputs\n",
        "for i in range(num_outputs):\n",
        "  out_layer_name = \"LLR_bit_\" + str(i+1)\n",
        "  bit_layer = Dense(4, activation  = 'relu', name = \"bit_layer_\" + str(i + 1))(x)\n",
        "  output_vec[i] = Dense(1, activation = \"linear\", name = out_layer_name)(bit_layer)\n",
        "\n",
        "model = Model(inputs = model_input, outputs = output_vec)\n",
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "model_input (InputLayer)        [(None, 2)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 256)          768         model_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 128)          32896       dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 64)           8256        dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 32)           2080        dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 16)           528         dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 8)            136         dense_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_6 (Dense)                 (None, 8)            72          dense_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_1 (Dense)             (None, 4)            36          dense_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_2 (Dense)             (None, 4)            36          dense_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_3 (Dense)             (None, 4)            36          dense_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_4 (Dense)             (None, 4)            36          dense_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_1 (Dense)               (None, 1)            5           bit_layer_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_2 (Dense)               (None, 1)            5           bit_layer_2[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_3 (Dense)               (None, 1)            5           bit_layer_3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_4 (Dense)               (None, 1)            5           bit_layer_4[0][0]                \n",
            "==================================================================================================\n",
            "Total params: 44,900\n",
            "Trainable params: 44,900\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OACao-k4BNnN"
      },
      "source": [
        "## Train the model on single SNR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lTfp_fG7FMrv",
        "colab": {}
      },
      "source": [
        "# Experimented with model parameters:\n",
        "# Can choose loss function, metrics, optimizer parameters\n",
        "model.compile(loss='logcosh',\n",
        "              optimizer = optimizers.Nadam(),\n",
        "              metrics = ['mean_squared_error'])\n",
        "\n",
        "# Since we have one output per bit, store the results of each its own list\n",
        "y_train_flattened = [None] * num_outputs\n",
        "y_valid_flattened = [None] * num_outputs\n",
        "y_test_flattened = [None] * num_outputs\n",
        "\n",
        "# Load the list with the LLRs of each bit\n",
        "for i in range(num_outputs):\n",
        "  y_train_flattened[i] = y_train_single_SNR[:,i]\n",
        "  y_valid_flattened[i] = y_valid_single_SNR[:,i]\n",
        "  y_test_flattened[i] = y_test_single_SNR[:,i]\n",
        "\n",
        "# Train the model finally\n",
        "history = model.fit(X_train_single_SNR, y_train_flattened, validation_data=(X_valid_single_SNR, y_valid_flattened), batch_size=50, epochs=100, verbose = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RPbbX8COBQMT"
      },
      "source": [
        "## Evaluate the Model on the same SNR\n",
        "<a id='16_QAM_train_test_2dB'></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5LqbmULwsdMT",
        "outputId": "cf9a1327-b4b3-4bda-8681-6abadb57b762",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# Test model on the validation and test set and report results\n",
        "score_val = model.evaluate(X_valid_single_SNR, y_valid_flattened, batch_size = 100, verbose=0)\n",
        "score_test = model.evaluate(X_test_single_SNR, y_test_flattened, batch_size = 100, verbose=0)\n",
        "\n",
        "print(\"Evaluate model on the validation set:\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Total Loss: \\t\\t\\t\", score_val[0])\n",
        "for i in range(num_outputs):\n",
        "  print(\"Bit \" + str(i) + \" [Loss, MSE] is: \\t\\t\", score_val[i+ (num_outputs != 1)], \"\\t, \", score_val[i+ 1 + 2*(num_outputs != 1)])\n",
        "\n",
        "print(\"\\n\\nEvaluate model on the test set:\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Total Loss: \\t\\t\\t\", score_test[0])\n",
        "for i in range(num_outputs):\n",
        "  print(\"Bit \" + str(i) + \" [Loss, MSE] is: \\t\\t\", score_test[i+ (num_outputs != 1)], \"\\t, \", score_test[i+ 1 + 2*(num_outputs != 1)])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluate model on the validation set:\n",
            "---------------------------------------------------\n",
            "Total Loss: \t\t\t 0.00017986339225899427\n",
            "Bit 0 [Loss, MSE] is: \t\t 4.224625e-05 \t,  5.4500197e-05\n",
            "Bit 1 [Loss, MSE] is: \t\t 3.1152238e-05 \t,  5.1964707e-05\n",
            "Bit 2 [Loss, MSE] is: \t\t 5.4500197e-05 \t,  8.453977e-05\n",
            "Bit 3 [Loss, MSE] is: \t\t 5.1964707e-05 \t,  6.231358e-05\n",
            "\n",
            "\n",
            "Evaluate model on the test set:\n",
            "---------------------------------------------------\n",
            "Total Loss: \t\t\t 0.00016866054550822204\n",
            "Bit 0 [Loss, MSE] is: \t\t 3.4301724e-05 \t,  5.0651943e-05\n",
            "Bit 1 [Loss, MSE] is: \t\t 3.195665e-05 \t,  5.175023e-05\n",
            "Bit 2 [Loss, MSE] is: \t\t 5.0651943e-05 \t,  6.8617155e-05\n",
            "Bit 3 [Loss, MSE] is: \t\t 5.175023e-05 \t,  6.3921405e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "je6LYZDlBczS"
      },
      "source": [
        "### Plot the LLRs acquired from the neural net vs actual LLRs`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aTyAbuVtBwsy",
        "outputId": "d1f5ff70-720b-493a-881c-fe10ba02a760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "y_pred = [None]*num_outputs\n",
        "y_pred = model.predict(X_test_single_SNR)\n",
        "\n",
        "f = plt.figure()\n",
        "f.set_figheight(10)\n",
        "f.set_figwidth(10)\n",
        "for i in range(num_outputs):\n",
        "  if num_outputs != 1:\n",
        "    predicted = y_pred[i][:]\n",
        "  else:\n",
        "    predicted = y_pred\n",
        "\n",
        "  plt.subplot(2,2,i+1)\n",
        "  plt.scatter(np.asarray(predicted), y_test_single_SNR[:,i].reshape([-1,1]))\n",
        "  plt.plot(y_test_single_SNR[:,i].reshape([-1,1]), y_test_single_SNR[:,i].reshape([-1,1]), 'k')\n",
        "  plt.xlabel(\"Predicted Neural Network LLRs\")\n",
        "  plt.ylabel(\"Actual LLRs\")\n",
        "  plt.title(\"Bit \" + str(i) + \": \" + MODULATION)\n",
        "  plt.grid()\n",
        "\n",
        "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
        "f.legend(('Results if model was 100% Accurate', 'Results', ), loc = 'lower center')\n",
        "f.suptitle(\"Results for Model trained and tested on \" + str(SNR) + \" dB\")\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wB+Cj2BXhyeZM4DWgF9AXmLylHZrZaOCmsE6dgKHAAOAVM8uJKX4GNbedy4G9zaxbTHm1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+HMAX6phpzyMV+H0tPw1ornAACj730AvW8dBPNd54Bp4KgzanXFQZCuVpOr1QO4Zk1UNaXUnytZ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\npKSkyJ49e0bl5OToe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            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-xvj30smjcd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model.save('llr_bpsk_model_single.h5', include_optimizer=False)\n",
        "\n",
        "# !python3 keras_export/convert_model.py llr_bpsk_model_single.h5 bpsk_single.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2L3XpK9jELEs"
      },
      "source": [
        "## Evaluate the Model on various SNR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aYO0sGOjEV4-",
        "outputId": "e1e95728-d557-4cae-9327-24b7ddf053c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "y_test_flattened = [None] * num_outputs\n",
        "\n",
        "for i in range(num_outputs):\n",
        "  y_test_flattened[i] = y_test_multiple_SNR[:,i]\n",
        "\n",
        "# Test model on the validation and test set and report results\n",
        "score_test = model.evaluate(X_test_multiple_SNR, y_test_flattened, batch_size = 100, verbose=0)\n",
        "\n",
        "print(\"Evaluate model on multiple SNR:\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Total Loss: \\t\\t\\t\", score_test[0])\n",
        "for i in range(num_outputs):\n",
        "  print(\"Bit \" + str(i) + \" [Loss, MSE] is: \\t\\t\", score_test[i+ (num_outputs != 1)], \"\\t, \", score_test[i+ 1 + 2*(num_outputs != 1)])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluate model on multiple SNR:\n",
            "---------------------------------------------------\n",
            "Total Loss: \t\t\t 6.250434187833559\n",
            "Bit 0 [Loss, MSE] is: \t\t 0.798005 \t,  0.7973316\n",
            "Bit 1 [Loss, MSE] is: \t\t 2.3258762 \t,  2.3292215\n",
            "Bit 2 [Loss, MSE] is: \t\t 0.7973316 \t,  3.5742443\n",
            "Bit 3 [Loss, MSE] is: \t\t 2.3292215 \t,  22.901434\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AUyejsFYTE8-",
        "colab_type": "text"
      },
      "source": [
        "### Plot the LLRs acquired from the neural net vs actual LLRs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YT_micrKTFfd",
        "colab_type": "code",
        "outputId": "9713093c-96fc-41ec-b211-4c0c6413a0a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "y_pred = [None]*num_outputs\n",
        "y_pred = model.predict(X_test_multiple_SNR)\n",
        "\n",
        "f = plt.figure()\n",
        "f.set_figheight(10)\n",
        "f.set_figwidth(10)\n",
        "for i in range(num_outputs):\n",
        "  if num_outputs != 1:\n",
        "    predicted = y_pred[i][:]\n",
        "  else:\n",
        "    predicted = y_pred\n",
        "\n",
        "  plt.subplot(2,2,i+1)\n",
        "  plt.scatter(np.asarray(predicted), y_test_multiple_SNR[:,i].reshape([-1,1]))\n",
        "  plt.plot(y_test_multiple_SNR[:,i].reshape([-1,1]), y_test_multiple_SNR[:,i].reshape([-1,1]), 'k')\n",
        "  plt.xlabel(\"Predicted Neural Network LLRs\")\n",
        "  plt.ylabel(\"Actual LLRs\")\n",
        "  plt.title(\"Bit \" + str(i) + \": \" + MODULATION)\n",
        "  plt.grid()\n",
        "\n",
        "f.legend(('Results if model was 100% Accurate', 'Results', ), loc = 'lower center')\n",
        "f.suptitle(\"Results for Model trained on \" + str(SNR) + \" dB\" \" and tested on [\" + str(SNR_RANGE_MIN) + \", \" + str(SNR_RANGE_MAX) + \"]\" )\n",
        "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e+jWHT55D7kKb3cpfr169fAlaUVFRQhI0sCTNeEDuwnwqQ1SkVahaAVJPkyZN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VGVlbR3pyqTwu7wXHVJDdrwOQRcUfIETYDXdSJgZUNajqrSKKr2DEqeoAG8cksrt8Wnyk+xbdJE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\n2ZAmaUoppZRSNvT/Adlt0RL093pPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KENSSMRFUaaq",
        "colab_type": "text"
      },
      "source": [
        "# Construct Fully Connected Neural Net trained on multiple SNR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fm9m_wwAXXWH",
        "colab_type": "text"
      },
      "source": [
        "## Define the second model. Note that we have multiple outputs corresponding to each bit LLR."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiCiG82PXj-h",
        "colab_type": "code",
        "outputId": "693c8f4b-cd20-4571-ee67-e533ea12035f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "model_input = Input(shape=(X_train_multiple_SNR.shape[1]), name='model_input')\n",
        "num_outputs = y_train_multiple_SNR.shape[1]\n",
        "num_layers = 6\n",
        "\n",
        "x = Dense(128, activation='relu')(model_input)\n",
        "for i in range(num_layers, 1, -1):\n",
        "  x = Dense(max(num_outputs*2, 2**i), activation=\"relu\")(x)\n",
        "\n",
        "output_vec = [None]*num_outputs\n",
        "for i in range(num_outputs):\n",
        "  out_layer_name = \"LLR_bit_\" + str(i+1)\n",
        "  bit_layer = Dense(4, activation  = 'relu', name = \"bit_layer_\" + str(i + 1))(x)\n",
        "  output_vec[i] = Dense(1, activation = \"linear\", name = out_layer_name)(bit_layer)\n",
        "\n",
        "model_multiple_snr = Model(inputs = model_input, outputs = output_vec)\n",
        "model_multiple_snr.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "model_input (InputLayer)        [(None, 2)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_7 (Dense)                 (None, 128)          384         model_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dense_8 (Dense)                 (None, 64)           8256        dense_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_9 (Dense)                 (None, 32)           2080        dense_8[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_10 (Dense)                (None, 16)           528         dense_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_11 (Dense)                (None, 8)            136         dense_10[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_12 (Dense)                (None, 8)            72          dense_11[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_1 (Dense)             (None, 4)            36          dense_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_2 (Dense)             (None, 4)            36          dense_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_3 (Dense)             (None, 4)            36          dense_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_4 (Dense)             (None, 4)            36          dense_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_1 (Dense)               (None, 1)            5           bit_layer_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_2 (Dense)               (None, 1)            5           bit_layer_2[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_3 (Dense)               (None, 1)            5           bit_layer_3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_4 (Dense)               (None, 1)            5           bit_layer_4[0][0]                \n",
            "==================================================================================================\n",
            "Total params: 11,620\n",
            "Trainable params: 11,620\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDOwt8ypXkae",
        "colab_type": "text"
      },
      "source": [
        "## Train the model on multiple SNR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dEvgbfhYF-z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Experimented with model parameters:\n",
        "# Can choose loss function, metrics, optimizer parameters\n",
        "model_multiple_snr.compile(loss='logcosh',\n",
        "              optimizer = optimizers.Nadam(),\n",
        "              metrics = ['mean_squared_error'])\n",
        "\n",
        "# Since we have one output per bit, store the results of each its own list\n",
        "y_train_flattened = [None] * num_outputs\n",
        "y_valid_flattened = [None] * num_outputs\n",
        "y_test_flattened = [None] * num_outputs\n",
        "\n",
        "for i in range(num_outputs):\n",
        "  y_train_flattened[i] = y_train_multiple_SNR[:,i]\n",
        "  y_valid_flattened[i] = y_valid_multiple_SNR[:,i]\n",
        "  y_test_flattened[i] = y_test_multiple_SNR[:,i]\n",
        "\n",
        "history = model_multiple_snr.fit(X_train_multiple_SNR, y_train_flattened, validation_data=(X_valid_multiple_SNR, y_valid_flattened), batch_size=50, epochs=100, verbose = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJro6fCHYGf7",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate the model on a various SNR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KpKZzdtYKbS",
        "colab_type": "code",
        "outputId": "9f5a6c16-8653-4011-a125-56f9252b0772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "y_valid_flattened = [None] * num_outputs\n",
        "y_test_flattened = [None] * num_outputs\n",
        "\n",
        "for i in range(num_outputs):\n",
        "  y_valid_flattened[i] = y_valid_multiple_SNR[:,i]\n",
        "  y_test_flattened[i] = y_test_multiple_SNR[:,i]\n",
        "\n",
        "# Test model on the validation and test set and report results\n",
        "score_val = model_multiple_snr.evaluate(X_valid_multiple_SNR, y_valid_flattened, batch_size = 100, verbose=0)\n",
        "score_test = model_multiple_snr.evaluate(X_test_multiple_SNR, y_test_flattened, batch_size = 100, verbose=0)\n",
        "\n",
        "print(\"Evaluate model on the validation set:\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Total Loss: \\t\\t\\t\", score_val[0])\n",
        "for i in range(num_outputs):\n",
        "  print(\"Bit \" + str(i) + \" [Loss, MSE] is: \\t\\t\", score_val[i+ (num_outputs != 1)], \"\\t, \", score_val[i+ 1 + 2*(num_outputs != 1)])\n",
        "\n",
        "print(\"\\n\\nEvaluate model on the test set:\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Total Loss: \\t\\t\\t\", score_test[0])\n",
        "for i in range(num_outputs):\n",
        "  print(\"Bit \" + str(i) + \" [Loss, MSE] is: \\t\\t\", score_test[i+ (num_outputs != 1)], \"\\t, \", score_test[i+ 1 + 2*(num_outputs != 1)])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluate model on the validation set:\n",
            "---------------------------------------------------\n",
            "Total Loss: \t\t\t 4.616531531016032\n",
            "Bit 0 [Loss, MSE] is: \t\t 0.57796293 \t,  0.6092434\n",
            "Bit 1 [Loss, MSE] is: \t\t 1.871766 \t,  1.8416798\n",
            "Bit 2 [Loss, MSE] is: \t\t 0.6092434 \t,  1.9490647\n",
            "Bit 3 [Loss, MSE] is: \t\t 1.8416798 \t,  13.412544\n",
            "\n",
            "\n",
            "Evaluate model on the test set:\n",
            "---------------------------------------------------\n",
            "Total Loss: \t\t\t 4.735565575686368\n",
            "Bit 0 [Loss, MSE] is: \t\t 0.5645975 \t,  0.5655713\n",
            "Bit 1 [Loss, MSE] is: \t\t 1.7848042 \t,  1.8205925\n",
            "Bit 2 [Loss, MSE] is: \t\t 0.5655713 \t,  2.0251794\n",
            "Bit 3 [Loss, MSE] is: \t\t 1.8205925 \t,  13.385569\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-92GqNJcGt2",
        "colab_type": "text"
      },
      "source": [
        "### Plot the LLRs acquired from the neural net vs actual LLRs`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9qp8ghCcHkg",
        "colab_type": "code",
        "outputId": "d22a5858-82e3-4b5f-96d9-6cdb4873a66a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "y_pred = [None]*num_outputs\n",
        "y_pred = model_multiple_snr.predict(X_test_multiple_SNR)\n",
        "\n",
        "f = plt.figure()\n",
        "f.set_figheight(10)\n",
        "f.set_figwidth(10)\n",
        "for i in range(num_outputs):\n",
        "  if num_outputs != 1:\n",
        "    predicted = y_pred[i][:]\n",
        "  else:\n",
        "    predicted = y_pred\n",
        "\n",
        "  plt.subplot(2,2,i+1)\n",
        "  plt.scatter(np.asarray(predicted), y_test_multiple_SNR[:,i].reshape([-1,1]))\n",
        "  plt.plot(y_test_multiple_SNR[:,i].reshape([-1,1]), y_test_multiple_SNR[:,i].reshape([-1,1]), 'k')\n",
        "  plt.xlabel(\"Predicted Neural Network LLRs\")\n",
        "  plt.ylabel(\"Actual LLRs\")\n",
        "  plt.title(\"Bit \" + str(i) + \": \" + MODULATION)\n",
        "  plt.grid()\n",
        "\n",
        "f.legend(('Results if model was 100% Accurate', 'Results', ), loc = 'lower center')\n",
        "f.suptitle(\"Results for Model trained on [\" + str(SNR_RANGE_MIN) + \", \" + str(SNR_RANGE_MAX) + \"]\" + \" dB\" \" and tested on [\" + str(SNR_RANGE_MIN) + \", \" + str(SNR_RANGE_MAX) + \"]\" )\n",
        "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jjDHGpCBL0owxxhhjUpAlacYYY4wxKciSNGOMMcaYFGRJmjHGGGNMCrIkzRhjjDEmBVmS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5IFB/hxNsdBo/AQJvZrpT3pPaC8fz0tmd1BRAJxJmjzjDELC28vmLQZY5aIyBsiUs8Yc8yK6yvP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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKThSdPRv4k9",
        "colab_type": "text"
      },
      "source": [
        "# Construct Fully Connected Nerual Net trained on Single SNR with Fading"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ZzwJVDqwEeV",
        "colab_type": "text"
      },
      "source": [
        "## Define the third model. Note that we have multiple outputs corresponding to each bit LLR."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NQ6opMhv-Uo",
        "colab_type": "code",
        "outputId": "edb7779b-7157-4d7d-ca51-ac7575d11283",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        }
      },
      "source": [
        "model_input = Input(shape=(X_train_single_SNR_fade.shape[1]), name='model_input')\n",
        "num_outputs = y_train_single_SNR_fade.shape[1]\n",
        "num_layers = 6\n",
        "\n",
        "x = Dense(128, activation='relu')(model_input)\n",
        "x = Dropout(0.5)(x)\n",
        "for i in range(num_layers, 1, -1):\n",
        "  x = Dense(max(num_outputs*2, 2**i), activation=\"relu\")(x)\n",
        "\n",
        "output_vec = [None]*num_outputs\n",
        "for i in range(num_outputs):\n",
        "  out_layer_name = \"LLR_bit_\" + str(i+1)\n",
        "  bit_layer = Dense(4, activation  = 'relu', name = \"bit_layer_\" + str(i + 1))(x)\n",
        "  output_vec[i] = Dense(1, activation = \"linear\", name = out_layer_name)(bit_layer)\n",
        "\n",
        "model_fade = Model(inputs = model_input, outputs = output_vec)\n",
        "model_fade.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "model_input (InputLayer)        [(None, 2)]          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_13 (Dense)                (None, 128)          384         model_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 128)          0           dense_13[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_14 (Dense)                (None, 64)           8256        dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_15 (Dense)                (None, 32)           2080        dense_14[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_16 (Dense)                (None, 16)           528         dense_15[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_17 (Dense)                (None, 8)            136         dense_16[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_18 (Dense)                (None, 8)            72          dense_17[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_1 (Dense)             (None, 4)            36          dense_18[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_2 (Dense)             (None, 4)            36          dense_18[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_3 (Dense)             (None, 4)            36          dense_18[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bit_layer_4 (Dense)             (None, 4)            36          dense_18[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_1 (Dense)               (None, 1)            5           bit_layer_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_2 (Dense)               (None, 1)            5           bit_layer_2[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_3 (Dense)               (None, 1)            5           bit_layer_3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "LLR_bit_4 (Dense)               (None, 1)            5           bit_layer_4[0][0]                \n",
            "==================================================================================================\n",
            "Total params: 11,620\n",
            "Trainable params: 11,620\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZXaOraJweYL",
        "colab_type": "text"
      },
      "source": [
        "## Train the model on faded symbols with AWGN noise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQ085R6Ewg2f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Experimented with model parameters:\n",
        "# Can choose loss function, metrics, optimizer parameters\n",
        "model_fade.compile(loss='logcosh',\n",
        "              optimizer = optimizers.Nadam(),\n",
        "              metrics = ['mean_squared_error'])\n",
        "\n",
        "# Since we have one output per bit, store the results of each its own list\n",
        "y_train_flattened = [None] * num_outputs\n",
        "y_valid_flattened = [None] * num_outputs\n",
        "y_test_flattened = [None] * num_outputs\n",
        "\n",
        "for i in range(num_outputs):\n",
        "  y_train_flattened[i] = y_train_single_SNR_fade[:,i]\n",
        "  y_valid_flattened[i] = y_valid_single_SNR_fade[:,i]\n",
        "  y_test_flattened[i] = y_test_single_SNR_fade[:,i]\n",
        "\n",
        "history = model_fade.fit(X_train_single_SNR_fade, y_train_flattened, validation_data=(X_valid_single_SNR_fade, y_valid_flattened), batch_size=50, epochs=100, verbose = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etnFFfie9JcS",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate the model on single SNR with fading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqmB8wYvzDuG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e2eb0f65-a210-4c5f-815f-971333dc0aa9"
      },
      "source": [
        "y_valid_flattened = [None] * num_outputs\n",
        "y_test_flattened = [None] * num_outputs\n",
        "\n",
        "for i in range(num_outputs):\n",
        "  y_valid_flattened[i] = y_valid_single_SNR_fade[:,i]\n",
        "  y_test_flattened[i] = y_test_single_SNR_fade[:,i]\n",
        "\n",
        "# Test model on the validation and test set and report results\n",
        "score_val = model_fade.evaluate(X_valid_single_SNR_fade, y_valid_flattened, batch_size = 100, verbose=0)\n",
        "score_test = model_fade.evaluate(X_test_single_SNR_fade, y_test_flattened, batch_size = 100, verbose=0)\n",
        "\n",
        "print(\"Evaluate model on the validation set:\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Total Loss: \\t\\t\\t\", score_val[0])\n",
        "for i in range(num_outputs):\n",
        "  print(\"Bit \" + str(i) + \" [Loss, MSE] is: \\t\\t\", score_val[i+ int(num_outputs != 1)], \"\\t, \", score_val[i+ 1 + 2*int(num_outputs != 1)])\n",
        "\n",
        "print(\"\\n\\nEvaluate model on the test set:\")\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Total Loss: \\t\\t\\t\", score_test[0])\n",
        "for i in range(num_outputs):\n",
        "  print(\"Bit \" + str(i) + \" [Loss, MSE] is: \\t\\t\", score_test[i+ int(num_outputs != 1)], \"\\t, \", score_test[i+ 1 + 2*int(num_outputs != 1)])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluate model on the validation set:\n",
            "---------------------------------------------------\n",
            "Total Loss: \t\t\t 0.7397337873776754\n",
            "Bit 0 [Loss, MSE] is: \t\t 0.19354798 \t,  0.08809429\n",
            "Bit 1 [Loss, MSE] is: \t\t 0.25146192 \t,  0.20662959\n",
            "Bit 2 [Loss, MSE] is: \t\t 0.08809429 \t,  0.49068308\n",
            "Bit 3 [Loss, MSE] is: \t\t 0.20662959 \t,  0.61568975\n",
            "\n",
            "\n",
            "Evaluate model on the test set:\n",
            "---------------------------------------------------\n",
            "Total Loss: \t\t\t 0.7269990742206573\n",
            "Bit 0 [Loss, MSE] is: \t\t 0.19760829 \t,  0.08481427\n",
            "Bit 1 [Loss, MSE] is: \t\t 0.24594508 \t,  0.19863145\n",
            "Bit 2 [Loss, MSE] is: \t\t 0.08481427 \t,  0.5023234\n",
            "Bit 3 [Loss, MSE] is: \t\t 0.19863145 \t,  0.6033409\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7h3lipi-dnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "9c89a974-1b33-4c23-d956-322aeef244e1"
      },
      "source": [
        "y_pred = [None]*num_outputs\n",
        "y_pred = model_fade.predict(X_test_single_SNR_fade)\n",
        "\n",
        "f = plt.figure()\n",
        "f.set_figheight(10)\n",
        "f.set_figwidth(10)\n",
        "for i in range(num_outputs):\n",
        "  if num_outputs != 1:\n",
        "    predicted = y_pred[i][:]\n",
        "  else:\n",
        "    predicted = y_pred\n",
        "\n",
        "  plt.subplot(2,2,i+1)\n",
        "  plt.scatter(np.asarray(predicted), y_test_single_SNR_fade[:,i].reshape([-1,1]))\n",
        "  plt.plot(y_test_single_SNR_fade[:,i].reshape([-1,1]), y_test_single_SNR_fade[:,i].reshape([-1,1]), 'k')\n",
        "  plt.xlabel(\"Predicted Neural Network LLRs\")\n",
        "  plt.ylabel(\"Actual LLRs\")\n",
        "  plt.title(\"Bit \" + str(i) + \": \" + MODULATION)\n",
        "  plt.grid()\n",
        "\n",
        "f.legend(('Results if model was 100% Accurate', 'Results', ), loc = 'lower center')\n",
        "f.suptitle(\"Model trained and tested on Rayleigh fading channel with variance \" + str(FADE_VAR) + \" and AWGN noise\")\n",
        "plt.subplots_adjust(wspace=0.5, hspace=0.5)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W/YmgYrrdzNpb0DF+azM7MIl45hKcgdR3f6p1Cc7YFplZZ4J+OOmYACyxoANwm7C1Yxcz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Fv3v5O8zjdVuCwPqn53HOUaJSZvIhKCtXfMT0S6YO2oAaiPdVHJykoCCq8PEcZfExMAMMacKvTl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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}